{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX E MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Apr 2025, 18:32:26
{txt}
{com}. 
. 
. 
. 
. *** APPENDIX E MODELS: DECOMPOSITION OF RELATIVE TYPE I ERROR RATES BETWEEN PROGRAM UNDERPAYMENT ERRORS AND PROGRAM DENIAL ERRORS (MODEL E1 & MODEL E2) ***
. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. 
. *** MODELS PREDICTING VARIOUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
. 
. 
. 
. 
. 
. *** MODEL E1: RELATIVE TYPE I ERROR RATE: TYPE I ERRORS RELATIVE TO UNDERPAYMENT TYPE II ERRORS (SAMPLE WEIGHTED)***
. 
. * [# overpayment errors / paid claims sample] / ([# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# underpayment errors / denied claims sample])
. 
. 
. 
. 
. 
. *** MODEL E2: RELATIVE TYPE I ERROR RATE: TYPE I ERRORS RELATIVE TO ERRONEOUS DENIAL TYPE II ERRORS (SAMPLE WEIGHTED)***
. 
. * [# overpayment errors / paid claims sample] / ([# overpayment errors / paid claims sample] + [# erroneous denial errors / denied claims sample])
. 
. 
. 
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** RETRIEVE MANUSCRIPT MODELS DATABASE [as of 04-10-2025] ***
. 
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", clear
{txt}
{com}. 
. 
. 
. 
. 
. 
. *** CREATE NEW RELATIVE TYPE I ERROR RATE VARIABLES UNIQUE FOR APPENDIX E [New: 05-07-2025] ***
. 
. 
. * Relative Type I Error Rate: ONLY Underpayment Type II Errors (MODEL E1)
. gen relt1error_up = t1error_rat/(t1error_rat + t2underp_pcrat + t2underp_dcrat)
{txt}(1,887 missing values generated)

{com}. *
. *
. * Relative Type I Error Rate: ONLY Erroneous Denial Type II Errors (MODEL E2)
. gen relt1error_denial = t1error_rat/(t1error_rat + t2denial_dcrat)
{txt}(1,506 missing values generated)

{com}. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
. 
. 
. 
. **** TABLE E1 -- MODELS E1-E2: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" APPENDIX E STATISTICAL ANALYSES [APRIL 2025]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [RELATIVE TYPE I PROGRAM ERROR RATE, DISAGGREGATED BY TYPE II UNDERPAYMENTS AND TYPE II ERRONEOUS DENIALS] **** 
. 
. 
. ** (MODEL E1; FIGURES E1A-E1C; MODEL E2: FIGURES E2A-E2C) **
. 
. 
. 
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS] ***
. 
. ** PURPOSE: COMPUTE MARGINAL DIFFERENTIAL EFFECTS IN MANUSCRIPT MODELS [BASED ON EFFECTIVE SAMPLE OF OBSERVATIONS] **
. 
. 
. 
. ** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS E1 & E2] **
. 
. 
. * Relative Type I Error Rate: MODEL E1 [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_up  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0217391              0       {txt}Obs         {res}      6,238
{txt}25%    {res} .0434988              0       {txt}Sum of wgt. {res}      6,238

{txt}50%    {res} .0833333                      {txt}Mean          {res}  .106841
                        {txt}Largest       Std. dev.     {res} .0996023
{txt}75%    {res} .1388889       .7222222
{txt}90%    {res} .2177177       .7738096       {txt}Variance      {res} .0099206
{txt}95%    {res} .2888889        .781746       {txt}Skewness      {res} 2.336603
{txt}99%    {res} .5357143       .7857143       {txt}Kurtosis      {res} 11.05856
{txt}
{com}. di r(p75)
{res}.1388889
{txt}
{com}. di r(p25)
{res}.04349882
{txt}
{com}. *
. gen relt1_interstate_catE1 =.
{txt}(12,551 missing values generated)

{com}. replace relt1_interstate_catE1 = 0 if tot_interstate<= r(p25) 
{txt}(2,398 real changes made)

{com}. replace relt1_interstate_catE1 = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,497 real changes made)

{com}. replace relt1_interstate_catE1 = 2 if tot_interstate>= r(p75) 
{txt}(4,656 real changes made)

{com}. *
. tab relt1_interstate_catE1

{txt}relt1_inter {c |}
state_catE1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,398       19.11       19.11
{txt}          1 {c |}{res}      5,497       43.80       62.90
{txt}          2 {c |}{res}      4,656       37.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab relt1_interstate_catE1 if itmod_adopt_state==1

{txt}relt1_inter {c |}
state_catE1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,728       23.40       23.40
{txt}          1 {c |}{res}      3,405       46.10       69.50
{txt}          2 {c |}{res}      2,253       30.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. *
. *
. *
. *
. 
. * Relative Type I Error Rate: MODEL E2 [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_denial  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      6,507
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      6,507

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1086318
                        {txt}Largest       Std. dev.     {res} .1012502
{txt}75%    {res} .1416667       .7738096
{txt}90%    {res} .2222222       .7785548       {txt}Variance      {res} .0102516
{txt}95%    {res} .2928572        .781746       {txt}Skewness      {res} 2.333386
{txt}99%    {res} .5392157       .7857143       {txt}Kurtosis      {res} 10.96298
{txt}
{com}. di r(p75)
{res}.14166668
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen relt1_interstate_catE2 =.
{txt}(12,551 missing values generated)

{com}. replace relt1_interstate_catE2 = 0 if tot_interstate<= r(p25) 
{txt}(2,546 real changes made)

{com}. replace relt1_interstate_catE2 = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,512 real changes made)

{com}. replace relt1_interstate_catE2 = 2 if tot_interstate>= r(p75) 
{txt}(4,493 real changes made)

{com}. *
. tab relt1_interstate_catE2  if e(sample)

{txt}relt1_inter {c |}
state_catE2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,689       25.96       25.96
{txt}          1 {c |}{res}      3,191       49.04       75.00
{txt}          2 {c |}{res}      1,627       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,507      100.00
{txt}
{com}. tab relt1_interstate_catE2  if e(sample) & itmod_adopt_state==1

{txt}relt1_inter {c |}
state_catE2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,689       25.96       25.96
{txt}          1 {c |}{res}      3,191       49.04       75.00
{txt}          2 {c |}{res}      1,627       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,507      100.00
{txt}
{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. ** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS E1 & E2] **
. 
. 
. * Relative Type I Error Rate: MODEL E1 [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_up  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1358859       .0181818
{txt} 5%    {res} .2142857       .0277778
{txt}10%    {res} .2770482       .0527778       {txt}Obs         {res}      6,238
{txt}25%    {res} .3956522       .0548048       {txt}Sum of wgt. {res}      6,238

{txt}50%    {res} .5441743                      {txt}Mean          {res} .5483141
                        {txt}Largest       Std. dev.     {res} .2083566
{txt}75%    {res} .6904762       1.207547
{txt}90%    {res} .8290598       1.219512       {txt}Variance      {res} .0434125
{txt}95%    {res} .9055556       1.299107       {txt}Skewness      {res}  .213568
{txt}99%    {res} 1.052632       1.299107       {txt}Kurtosis      {res} 2.670131
{txt}
{com}. di r(p75)
{res}.69047618
{txt}
{com}. di r(p25)
{res}.39565217
{txt}
{com}. *
. gen relt1_diffoccupseek_catE1 =.
{txt}(12,551 missing values generated)

{com}. replace relt1_diffoccupseek_catE1 = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,137 real changes made)

{com}. replace relt1_diffoccupseek_catE1 = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(6,022 real changes made)

{com}. replace relt1_diffoccupseek_catE1 = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,392 real changes made)

{com}. *
. tab relt1_diffoccupseek_catE1 if e(sample)

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
        tE1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,560       25.01       25.01
{txt}          1 {c |}{res}      3,114       49.92       74.93
{txt}          2 {c |}{res}      1,564       25.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,238      100.00
{txt}
{com}. tab relt1_diffoccupseek_catE1 if e(sample) & itmod_adopt_state==1

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
        tE1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,560       25.01       25.01
{txt}          1 {c |}{res}      3,114       49.92       74.93
{txt}          2 {c |}{res}      1,564       25.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,238      100.00
{txt}
{com}. 
. *
. *
. *
. *
. 
. * Relative Type I Error Rate: MODEL E2 [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_denial  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1388889       .0277778
{txt} 5%    {res}  .218254       .0527778
{txt}10%    {res} .2780045       .0548048       {txt}Obs         {res}      6,507
{txt}25%    {res} .3966184       .0604082       {txt}Sum of wgt. {res}      6,507

{txt}50%    {res} .5444444                      {txt}Mean          {res} .5489455
                        {txt}Largest       Std. dev.     {res} .2069415
{txt}75%    {res} .6901271       1.207547
{txt}90%    {res} .8285714       1.219512       {txt}Variance      {res} .0428248
{txt}95%    {res} .9055556       1.299107       {txt}Skewness      {res} .2181071
{txt}99%    {res} 1.048387       1.299107       {txt}Kurtosis      {res} 2.676694
{txt}
{com}. di r(p75)
{res}.69012707
{txt}
{com}. di r(p25)
{res}.39661837
{txt}
{com}. *
. gen relt1_diffoccupseek_catE2 =.
{txt}(12,551 missing values generated)

{com}. replace relt1_diffoccupseek_catE2 = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,158 real changes made)

{com}. replace relt1_diffoccupseek_catE2 = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,996 real changes made)

{com}. replace relt1_diffoccupseek_catE2 = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,397 real changes made)

{com}. *
. tab relt1_diffoccupseek_catE2 if e(sample)

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
        tE2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,627       25.00       25.00
{txt}          1 {c |}{res}      3,253       49.99       75.00
{txt}          2 {c |}{res}      1,627       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,507      100.00
{txt}
{com}. tab relt1_diffoccupseek_catE2 if e(sample) & itmod_adopt_state==1

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
        tE2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,627       25.00       25.00
{txt}          1 {c |}{res}      3,253       49.99       75.00
{txt}          2 {c |}{res}      1,627       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,507      100.00
{txt}
{com}. 
. 
. 
. 
. 
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** ESTIMATE MODEL E1: RELATIVE TYPE I ERROR RATE (SAMPLE WEIGHTED): TYPE I ERRORS RELATIVE TO ONLY UNDERPAYMENT TYPE II ERRORS ***  (FIGURES E1A-E1C) 
. 
. * DV: [# overpayment errors / paid claims sample] / ([# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# underpayment errors / denied claims sample]) ***   
. 
. 
. npregress series relt1error_up itmod_monthcount i.relt1_interstate_catE1   i.relt1_diffoccupseek_catE1  if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat   i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2}-.3846083{col 40}{space 2} .0391208{col 51}{space 1}   -9.83{col 60}{space 3}0.000{col 68}{space 4}-.4612835{col 81}{space 3} -.307933
{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.4440161{col 40}{space 2} .0581997{col 51}{space 1}   -7.63{col 60}{space 3}0.000{col 68}{space 4}-.5580853{col 81}{space 3}-.3299469
{txt}{space 23}31  {c |}{col 28}{res}{space 2}-.4330763{col 40}{space 2} .1133953{col 51}{space 1}   -3.82{col 60}{space 3}0.000{col 68}{space 4}-.6553271{col 81}{space 3}-.2108255
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.1096219{col 40}{space 2} .0457961{col 51}{space 1}   -2.39{col 60}{space 3}0.017{col 68}{space 4}-.1993806{col 81}{space 3}-.0198632
{txt}{space 23}35  {c |}{col 28}{res}{space 2}-.3927806{col 40}{space 2} .0666566{col 51}{space 1}   -5.89{col 60}{space 3}0.000{col 68}{space 4} -.523425{col 81}{space 3}-.2621361
{txt}{space 23}38  {c |}{col 28}{res}{space 2}-.3286725{col 40}{space 2} .0615671{col 51}{space 1}   -5.34{col 60}{space 3}0.000{col 68}{space 4}-.4493418{col 81}{space 3}-.2080032
{txt}{space 23}40  {c |}{col 28}{res}{space 2}-.3056864{col 40}{space 2} .0361268{col 51}{space 1}   -8.46{col 60}{space 3}0.000{col 68}{space 4}-.3764936{col 81}{space 3}-.2348791
{txt}{space 23}42  {c |}{col 28}{res}{space 2}-.4030564{col 40}{space 2}  .035952{col 51}{space 1}  -11.21{col 60}{space 3}0.000{col 68}{space 4} -.473521{col 81}{space 3}-.3325918
{txt}{space 23}44  {c |}{col 28}{res}{space 2}-.4120012{col 40}{space 2} .0646901{col 51}{space 1}   -6.37{col 60}{space 3}0.000{col 68}{space 4}-.5387914{col 81}{space 3} -.285211
{txt}{space 23}46  {c |}{col 28}{res}{space 2}-.0906683{col 40}{space 2}   .04121{col 51}{space 1}   -2.20{col 60}{space 3}0.028{col 68}{space 4}-.1714385{col 81}{space 3}-.0098982
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.4951635{col 40}{space 2}  .051909{col 51}{space 1}   -9.54{col 60}{space 3}0.000{col 68}{space 4}-.5969033{col 81}{space 3}-.3934237
{txt}{space 23}50  {c |}{col 28}{res}{space 2}-.1321403{col 40}{space 2} .0684876{col 51}{space 1}   -1.93{col 60}{space 3}0.054{col 68}{space 4}-.2663734{col 81}{space 3} .0020929
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .0901587{col 40}{space 2} .0440789{col 51}{space 1}    2.05{col 60}{space 3}0.041{col 68}{space 4} .0037657{col 81}{space 3} .1765517
{txt}{space 23}52  {c |}{col 28}{res}{space 2}-.3690908{col 40}{space 2} .0430741{col 51}{space 1}   -8.57{col 60}{space 3}0.000{col 68}{space 4}-.4535145{col 81}{space 3}-.2846672
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} .0192891{col 40}{space 2} .0240774{col 51}{space 1}    0.80{col 60}{space 3}0.423{col 68}{space 4}-.0279017{col 81}{space 3} .0664798
{txt}{space 21}2004  {c |}{col 28}{res}{space 2} .0347061{col 40}{space 2} .0255735{col 51}{space 1}    1.36{col 60}{space 3}0.175{col 68}{space 4} -.015417{col 81}{space 3} .0848292
{txt}{space 21}2005  {c |}{col 28}{res}{space 2} .0350082{col 40}{space 2} .0269064{col 51}{space 1}    1.30{col 60}{space 3}0.193{col 68}{space 4}-.0177274{col 81}{space 3} .0877438
{txt}{space 21}2006  {c |}{col 28}{res}{space 2} .0269331{col 40}{space 2} .0274453{col 51}{space 1}    0.98{col 60}{space 3}0.326{col 68}{space 4}-.0268587{col 81}{space 3} .0807249
{txt}{space 21}2007  {c |}{col 28}{res}{space 2} .0085017{col 40}{space 2} .0286574{col 51}{space 1}    0.30{col 60}{space 3}0.767{col 68}{space 4}-.0476657{col 81}{space 3}  .064669
{txt}{space 21}2008  {c |}{col 28}{res}{space 2} .0478501{col 40}{space 2} .0270069{col 51}{space 1}    1.77{col 60}{space 3}0.076{col 68}{space 4}-.0050825{col 81}{space 3} .1007826
{txt}{space 21}2009  {c |}{col 28}{res}{space 2} .0926754{col 40}{space 2} .0296602{col 51}{space 1}    3.12{col 60}{space 3}0.002{col 68}{space 4} .0345425{col 81}{space 3} .1508083
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .1143402{col 40}{space 2} .0310981{col 51}{space 1}    3.68{col 60}{space 3}0.000{col 68}{space 4}  .053389{col 81}{space 3} .1752915
{txt}{space 21}2011  {c |}{col 28}{res}{space 2} .0800764{col 40}{space 2} .0308811{col 51}{space 1}    2.59{col 60}{space 3}0.010{col 68}{space 4} .0195505{col 81}{space 3} .1406024
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .1144063{col 40}{space 2} .0302821{col 51}{space 1}    3.78{col 60}{space 3}0.000{col 68}{space 4} .0550545{col 81}{space 3}  .173758
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} .0804348{col 40}{space 2} .0323366{col 51}{space 1}    2.49{col 60}{space 3}0.013{col 68}{space 4} .0170563{col 81}{space 3} .1438134
{txt}{space 21}2014  {c |}{col 28}{res}{space 2} .1168816{col 40}{space 2} .0345439{col 51}{space 1}    3.38{col 60}{space 3}0.001{col 68}{space 4} .0491769{col 81}{space 3} .1845863
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .1341091{col 40}{space 2} .0343763{col 51}{space 1}    3.90{col 60}{space 3}0.000{col 68}{space 4} .0667329{col 81}{space 3} .2014853
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}  .106335{col 40}{space 2} .0349976{col 51}{space 1}    3.04{col 60}{space 3}0.002{col 68}{space 4} .0377409{col 81}{space 3}  .174929
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .1098493{col 40}{space 2} .0371069{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} .0371211{col 81}{space 3} .1825775
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .1257303{col 40}{space 2} .0383796{col 51}{space 1}    3.28{col 60}{space 3}0.001{col 68}{space 4} .0505077{col 81}{space 3}  .200953
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0727968{col 40}{space 2}  .040693{col 51}{space 1}    1.79{col 60}{space 3}0.074{col 68}{space 4}  -.00696{col 81}{space 3} .1525537
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .2233989{col 40}{space 2} .0459384{col 51}{space 1}    4.86{col 60}{space 3}0.000{col 68}{space 4} .1333613{col 81}{space 3} .3134365
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .3911191{col 40}{space 2} .0444648{col 51}{space 1}    8.80{col 60}{space 3}0.000{col 68}{space 4} .3039698{col 81}{space 3} .4782684
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .3278099{col 40}{space 2} .0490532{col 51}{space 1}    6.68{col 60}{space 3}0.000{col 68}{space 4} .2316674{col 81}{space 3} .4239523
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .1869389{col 40}{space 2} .1347979{col 51}{space 1}    1.39{col 60}{space 3}0.166{col 68}{space 4}-.0772602{col 81}{space 3} .4511379
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2}  .226367{col 40}{space 2} .0869734{col 51}{space 1}    2.60{col 60}{space 3}0.009{col 68}{space 4} .0559024{col 81}{space 3} .3968317
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .3157346{col 40}{space 2}    .0952{col 51}{space 1}    3.32{col 60}{space 3}0.001{col 68}{space 4}  .129146{col 81}{space 3} .5023232
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2}-.0392163{col 40}{space 2} .0749941{col 51}{space 1}   -0.52{col 60}{space 3}0.601{col 68}{space 4} -.186202{col 81}{space 3} .1077694
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2}-.0493772{col 40}{space 2} .0769288{col 51}{space 1}   -0.64{col 60}{space 3}0.521{col 68}{space 4}-.2001549{col 81}{space 3} .1014004
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2}-.2318908{col 40}{space 2} .0766742{col 51}{space 1}   -3.02{col 60}{space 3}0.002{col 68}{space 4}-.3821695{col 81}{space 3}-.0816122
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2} .0463705{col 40}{space 2} .0688524{col 51}{space 1}    0.67{col 60}{space 3}0.501{col 68}{space 4}-.0885777{col 81}{space 3} .1813187
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2}  .025625{col 40}{space 2} .0807525{col 51}{space 1}    0.32{col 60}{space 3}0.751{col 68}{space 4}-.1326471{col 81}{space 3}  .183897
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0818642{col 40}{space 2} .0749347{col 51}{space 1}   -1.09{col 60}{space 3}0.275{col 68}{space 4}-.2287335{col 81}{space 3}  .065005
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2}-.0144227{col 40}{space 2} .0789764{col 51}{space 1}   -0.18{col 60}{space 3}0.855{col 68}{space 4}-.1692136{col 81}{space 3} .1403681
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0473682{col 40}{space 2} .0765739{col 51}{space 1}   -0.62{col 60}{space 3}0.536{col 68}{space 4}-.1974503{col 81}{space 3} .1027139
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.1037771{col 40}{space 2} .0881375{col 51}{space 1}   -1.18{col 60}{space 3}0.239{col 68}{space 4}-.2765235{col 81}{space 3} .0689693
{txt}{space 2}adoptcohort_2020_itadopt {c |}{col 28}{res}{space 2}-.0945573{col 40}{space 2} .0816951{col 51}{space 1}   -1.16{col 60}{space 3}0.247{col 68}{space 4}-.2546767{col 81}{space 3} .0655621
{txt}{space 2}adoptcohort_2021_itadopt {c |}{col 28}{res}{space 2}-.2213998{col 40}{space 2} .0801129{col 51}{space 1}   -2.76{col 60}{space 3}0.006{col 68}{space 4}-.3784181{col 81}{space 3}-.0643814
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m1e if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m1e if e(sample), residuals
{res}{txt}(6,313 missing values generated)

{com}. 
. gen sse_m1e = predsy_m1e * predsy_m1e if e(sample)
{txt}(6,313 missing values generated)

{com}. gen ssr_m1e = residsy_m1e * residsy_m1e if e(sample)
{txt}(6,313 missing values generated)

{com}. 
. egen sum_sse_m1e = total(sse_m1e) if e(sample)
{txt}(6,312 missing values generated)

{com}. egen sum_ssr_m1e = total(ssr_m1e) if e(sample)
{txt}(6,312 missing values generated)

{com}. 
. gen r2_m1e = sum_ssr_m1e/(sum_sse_m1e + sum_ssr_m1e)
{txt}(6,312 missing values generated)

{com}. 
. sum r2_m1e

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m1e {c |}{res}      6,239    .2583986           0   .2583986   .2583986
{txt}
{com}. 
. 
. 
. *
. *
. *
. * [MODEL E1: RELATIVE TYPE I ERROR RATE : UNDERPAYMENT TYPE II ERRORS] FIGURE E1A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,238}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5082973{col 26}{space 2} .0092634{col 37}{space 1}   54.87{col 46}{space 3}0.000{col 54}{space 4} .4901414{col 67}{space 3} .5264531
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5065623{col 26}{space 2}  .008541{col 37}{space 1}   59.31{col 46}{space 3}0.000{col 54}{space 4} .4898222{col 67}{space 3} .5233024
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4983395{col 26}{space 2} .0061091{col 37}{space 1}   81.57{col 46}{space 3}0.000{col 54}{space 4} .4863659{col 67}{space 3} .5103131
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4894137{col 26}{space 2} .0064996{col 37}{space 1}   75.30{col 46}{space 3}0.000{col 54}{space 4} .4766748{col 67}{space 3} .5021526
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4814338{col 26}{space 2} .0089215{col 37}{space 1}   53.96{col 46}{space 3}0.000{col 54}{space 4}  .463948{col 67}{space 3} .4989197
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4743139{col 26}{space 2} .0115625{col 37}{space 1}   41.02{col 46}{space 3}0.000{col 54}{space 4} .4516518{col 67}{space 3} .4969759
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .4679677{col 26}{space 2} .0139048{col 37}{space 1}   33.66{col 46}{space 3}0.000{col 54}{space 4} .4407147{col 67}{space 3} .4952207
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4623093{col 26}{space 2} .0158458{col 37}{space 1}   29.18{col 46}{space 3}0.000{col 54}{space 4} .4312522{col 67}{space 3} .4933664
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4572525{col 26}{space 2}  .017392{col 37}{space 1}   26.29{col 46}{space 3}0.000{col 54}{space 4} .4231649{col 67}{space 3} .4913402
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .4527114{col 26}{space 2} .0185869{col 37}{space 1}   24.36{col 46}{space 3}0.000{col 54}{space 4} .4162818{col 67}{space 3}  .489141
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4485998{col 26}{space 2} .0194891{col 37}{space 1}   23.02{col 46}{space 3}0.000{col 54}{space 4} .4104018{col 67}{space 3} .4867978
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4448317{col 26}{space 2} .0201644{col 37}{space 1}   22.06{col 46}{space 3}0.000{col 54}{space 4} .4053101{col 67}{space 3} .4843533
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE E1A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Program Error Rate: Underpayment Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E1]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1A.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. * [MODEL E1: RELATIVE TYPE I ERROR RATE : UNDERPAYMENT TYPE II ERRORS] Figure E1B (relt1_interstate_catE1==2) & LOW COMPLEXITY (relt1_interstate_catE1==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_catE1 if relt1_interstate_catE1==0|relt1_interstate_catE1==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,168}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_catE1@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.18{col 52}{space 2}   0.6713
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.40{col 52}{space 2}   0.5254
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     2.67{col 52}{space 2}   0.1021
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     5.49{col 52}{space 2}   0.0191
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     6.91{col 52}{space 2}   0.0086
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     7.58{col 52}{space 2}   0.0059
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     7.98{col 52}{space 2}   0.0047
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.27{col 52}{space 2}   0.0040
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.47{col 52}{space 2}   0.0036
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.56{col 52}{space 2}   0.0034
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.48{col 52}{space 2}   0.0036
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.14{col 52}{space 2}   0.0043
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_catE1@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2}-.0072405{col 40}{space 2} .0170638{col 51}{space 5}-.0406849{col 65}{space 3} .0262038
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2}-.0104782{col 40}{space 2} .0164999{col 51}{space 5}-.0428175{col 65}{space 3} .0218611
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2}-.0254764{col 40}{space 2} .0155846{col 51}{space 5}-.0560216{col 65}{space 3} .0050688
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2}-.0409352{col 40}{space 2} .0174669{col 51}{space 5}-.0751696{col 65}{space 3}-.0067007
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2}-.0537465{col 40}{space 2} .0204442{col 51}{space 5}-.0938163{col 65}{space 3}-.0136766
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2}-.0640399{col 40}{space 2} .0232547{col 51}{space 5}-.1096183{col 65}{space 3}-.0184616
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2}-.0719452{col 40}{space 2} .0254641{col 51}{space 5} -.121854{col 65}{space 3}-.0220365
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2}-.0775919{col 40}{space 2} .0269839{col 51}{space 5}-.1304794{col 65}{space 3}-.0247044
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2}-.0811096{col 40}{space 2} .0278677{col 51}{space 5}-.1357292{col 65}{space 3}  -.02649
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2}-.0826279{col 40}{space 2} .0282375{col 51}{space 5}-.1379724{col 65}{space 3}-.0272834
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2}-.0822765{col 40}{space 2} .0282561{col 51}{space 5}-.1376574{col 65}{space 3}-.0268957
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2} -.080185{col 40}{space 2} .0281116{col 51}{space 5}-.1352827{col 65}{space 3}-.0250873
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE E1B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity: Interstate Claims{c )-}" "{c -(}bf: (Relative Type I Program Error Rate: Underpayment Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E1]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1B.04-10-2025.gph} saved

{com}. *
. *
. *
. * [MODEL E1: RELATIVE TYPE I ERROR RATE : UNDERPAYMENT TYPE II ERRORS] Figure E1C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catE1==2) & LOW COMPLEXITY (relt1_diffoccupseek_catE1==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. margins r.relt1_diffoccupseek_catE1 if relt1_diffoccupseek_catE1==0|relt1_diffoccupseek_catE1==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,124}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 30}{c TT}{hline 11}{hline 12}{hline 11}
{col 31}{text}{c |}         df{col 43}        chi2{col 55}     P>chi2
{res}{col 1}{text}{hline 30}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_catE1@_at {c |}
{space 17}(2 vs 0)  1  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     6.07{col 55}{space 2}   0.0137
{txt}{space 17}(2 vs 0)  2  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     5.43{col 55}{space 2}   0.0198
{txt}{space 17}(2 vs 0)  3  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.86{col 55}{space 2}   0.1726
{txt}{space 17}(2 vs 0)  4  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.08{col 55}{space 2}   0.7818
{txt}{space 17}(2 vs 0)  5  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.11{col 55}{space 2}   0.7412
{txt}{space 17}(2 vs 0)  6  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.40{col 55}{space 2}   0.5265
{txt}{space 17}(2 vs 0)  7  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.60{col 55}{space 2}   0.4390
{txt}{space 17}(2 vs 0)  8  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.67{col 55}{space 2}   0.4143
{txt}{space 17}(2 vs 0)  9  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.62{col 55}{space 2}   0.4307
{txt}{space 17}(2 vs 0) 10  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.49{col 55}{space 2}   0.4841
{txt}{space 17}(2 vs 0) 11  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.31{col 55}{space 2}   0.5789
{txt}{space 17}(2 vs 0) 12  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.13{col 55}{space 2}   0.7224
{col 1}{text}                       Joint {col 31}{c |}{result}  (not testable)
{col 1}{text}{hline 30}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 31}{c |}{col 43} Delta-method
{col 31}{c |}   Contrast{col 43}   std. err.{col 55}     [95% con{col 68}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_catE1@_at {c |}
{space 17}(2 vs 0)  1  {c |}{col 31}{res}{space 2} .0386033{col 43}{space 2}  .015664{col 54}{space 5} .0079024{col 68}{space 3} .0693043
{txt}{space 17}(2 vs 0)  2  {c |}{col 31}{res}{space 2} .0351938{col 43}{space 2} .0151014{col 54}{space 5} .0055956{col 68}{space 3}  .064792
{txt}{space 17}(2 vs 0)  3  {c |}{col 31}{res}{space 2} .0197817{col 43}{space 2} .0145051{col 54}{space 5}-.0086477{col 68}{space 3} .0482112
{txt}{space 17}(2 vs 0)  4  {c |}{col 31}{res}{space 2} .0047296{col 43}{space 2} .0170749{col 54}{space 5}-.0287366{col 68}{space 3} .0381958
{txt}{space 17}(2 vs 0)  5  {c |}{col 31}{res}{space 2}-.0068078{col 43}{space 2} .0206149{col 54}{space 5}-.0472121{col 68}{space 3} .0335966
{txt}{space 17}(2 vs 0)  6  {c |}{col 31}{res}{space 2} -.015085{col 43}{space 2} .0238181{col 54}{space 5}-.0617676{col 68}{space 3} .0315976
{txt}{space 17}(2 vs 0)  7  {c |}{col 31}{res}{space 2}-.0203568{col 43}{space 2} .0263042{col 54}{space 5}-.0719121{col 68}{space 3} .0311986
{txt}{space 17}(2 vs 0)  8  {c |}{col 31}{res}{space 2}-.0228777{col 43}{space 2}  .028026{col 54}{space 5}-.0778076{col 68}{space 3} .0320523
{txt}{space 17}(2 vs 0)  9  {c |}{col 31}{res}{space 2}-.0229024{col 43}{space 2} .0290646{col 54}{space 5} -.079868{col 68}{space 3} .0340632
{txt}{space 17}(2 vs 0) 10  {c |}{col 31}{res}{space 2}-.0206856{col 43}{space 2} .0295629{col 54}{space 5}-.0786277{col 68}{space 3} .0372565
{txt}{space 17}(2 vs 0) 11  {c |}{col 31}{res}{space 2}-.0164819{col 43}{space 2} .0297007{col 54}{space 5}-.0746942{col 68}{space 3} .0417304
{txt}{space 17}(2 vs 0) 12  {c |}{col 31}{res}{space 2}-.0105459{col 43}{space 2} .0296818{col 54}{space 5}-.0687213{col 68}{space 3} .0476294
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE E1C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity: Seeking Different Occupation{c )-}" "{c -(}bf:(Relative Type I Program Error Rate: Underpayment Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E1]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E1.FIGURE E1C.04-10-2025.gph} saved

{com}. *
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. ***ESTIMATE MODEL E2: RELATIVE TYPE I ERROR RATE: (SAMPLE WEIGHTED): TYPE I ERRORS RELATIVE TO ONLY ERRONEOUS DENIAL TYPE II ERRORS  ***  (FIGURES E2A-E2C) 
. 
. * DV: [# overpayment errors / paid claims sample] / ([# overpayment errors / paid claims sample] + [# erroneous denial errors / denied claims sample])
. 
. 
. npregress series relt1error_denial  itmod_monthcount  i.relt1_interstate_catE2   i.relt1_diffoccupseek_catE2  if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2}-.1387992{col 40}{space 2} .0375961{col 51}{space 1}   -3.69{col 60}{space 3}0.000{col 68}{space 4}-.2124862{col 81}{space 3}-.0651122
{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.1999782{col 40}{space 2} .0535314{col 51}{space 1}   -3.74{col 60}{space 3}0.000{col 68}{space 4}-.3048977{col 81}{space 3}-.0950586
{txt}{space 23}31  {c |}{col 28}{res}{space 2}-.2910328{col 40}{space 2} .1327885{col 51}{space 1}   -2.19{col 60}{space 3}0.028{col 68}{space 4}-.5512936{col 81}{space 3}-.0307721
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0404743{col 40}{space 2} .0393165{col 51}{space 1}   -1.03{col 60}{space 3}0.303{col 68}{space 4}-.1175332{col 81}{space 3} .0365845
{txt}{space 23}35  {c |}{col 28}{res}{space 2}-.1765621{col 40}{space 2} .0732896{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4}-.3202071{col 81}{space 3}-.0329172
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .2475908{col 40}{space 2} .0613088{col 51}{space 1}    4.04{col 60}{space 3}0.000{col 68}{space 4} .1274277{col 81}{space 3} .3677538
{txt}{space 23}40  {c |}{col 28}{res}{space 2} .1083709{col 40}{space 2} .0396879{col 51}{space 1}    2.73{col 60}{space 3}0.006{col 68}{space 4}  .030584{col 81}{space 3} .1861578
{txt}{space 23}42  {c |}{col 28}{res}{space 2}-.0901002{col 40}{space 2} .0354883{col 51}{space 1}   -2.54{col 60}{space 3}0.011{col 68}{space 4} -.159656{col 81}{space 3}-.0205444
{txt}{space 23}44  {c |}{col 28}{res}{space 2}-.2427374{col 40}{space 2} .0710524{col 51}{space 1}   -3.42{col 60}{space 3}0.001{col 68}{space 4}-.3819975{col 81}{space 3}-.1034773
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0478975{col 40}{space 2} .0373194{col 51}{space 1}    1.28{col 60}{space 3}0.199{col 68}{space 4}-.0252472{col 81}{space 3} .1210423
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0192977{col 40}{space 2} .0509414{col 51}{space 1}   -0.38{col 60}{space 3}0.705{col 68}{space 4}-.1191411{col 81}{space 3} .0805457
{txt}{space 23}50  {c |}{col 28}{res}{space 2}-.1053522{col 40}{space 2} .0629577{col 51}{space 1}   -1.67{col 60}{space 3}0.094{col 68}{space 4} -.228747{col 81}{space 3} .0180427
{txt}{space 23}51  {c |}{col 28}{res}{space 2}-.0965165{col 40}{space 2} .0434918{col 51}{space 1}   -2.22{col 60}{space 3}0.026{col 68}{space 4}-.1817588{col 81}{space 3}-.0112741
{txt}{space 23}52  {c |}{col 28}{res}{space 2} .0322493{col 40}{space 2} .0433017{col 51}{space 1}    0.74{col 60}{space 3}0.456{col 68}{space 4}-.0526205{col 81}{space 3} .1171191
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} .0112705{col 40}{space 2} .0277531{col 51}{space 1}    0.41{col 60}{space 3}0.685{col 68}{space 4}-.0431245{col 81}{space 3} .0656655
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0824132{col 40}{space 2} .0279042{col 51}{space 1}   -2.95{col 60}{space 3}0.003{col 68}{space 4}-.1371045{col 81}{space 3}-.0277219
{txt}{space 21}2005  {c |}{col 28}{res}{space 2} -.086052{col 40}{space 2} .0293122{col 51}{space 1}   -2.94{col 60}{space 3}0.003{col 68}{space 4}-.1435028{col 81}{space 3}-.0286012
{txt}{space 21}2006  {c |}{col 28}{res}{space 2} -.123381{col 40}{space 2} .0289272{col 51}{space 1}   -4.27{col 60}{space 3}0.000{col 68}{space 4}-.1800772{col 81}{space 3}-.0666847
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.1349336{col 40}{space 2} .0304601{col 51}{space 1}   -4.43{col 60}{space 3}0.000{col 68}{space 4}-.1946344{col 81}{space 3}-.0752329
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.1650879{col 40}{space 2}  .029447{col 51}{space 1}   -5.61{col 60}{space 3}0.000{col 68}{space 4}-.2228031{col 81}{space 3}-.1073728
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.1089188{col 40}{space 2} .0322835{col 51}{space 1}   -3.37{col 60}{space 3}0.001{col 68}{space 4}-.1721933{col 81}{space 3}-.0456442
{txt}{space 21}2010  {c |}{col 28}{res}{space 2}-.0762194{col 40}{space 2} .0335816{col 51}{space 1}   -2.27{col 60}{space 3}0.023{col 68}{space 4}-.1420382{col 81}{space 3}-.0104006
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}-.1338887{col 40}{space 2}  .032307{col 51}{space 1}   -4.14{col 60}{space 3}0.000{col 68}{space 4}-.1972093{col 81}{space 3}-.0705681
{txt}{space 21}2012  {c |}{col 28}{res}{space 2}-.1389567{col 40}{space 2} .0322124{col 51}{space 1}   -4.31{col 60}{space 3}0.000{col 68}{space 4}-.2020919{col 81}{space 3}-.0758215
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} -.210369{col 40}{space 2}  .033764{col 51}{space 1}   -6.23{col 60}{space 3}0.000{col 68}{space 4}-.2765454{col 81}{space 3}-.1441927
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.2118375{col 40}{space 2} .0326332{col 51}{space 1}   -6.49{col 60}{space 3}0.000{col 68}{space 4}-.2757974{col 81}{space 3}-.1478776
{txt}{space 21}2015  {c |}{col 28}{res}{space 2}-.1706114{col 40}{space 2} .0345395{col 51}{space 1}   -4.94{col 60}{space 3}0.000{col 68}{space 4}-.2383076{col 81}{space 3}-.1029152
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}-.1505355{col 40}{space 2} .0350164{col 51}{space 1}   -4.30{col 60}{space 3}0.000{col 68}{space 4}-.2191663{col 81}{space 3}-.0819047
{txt}{space 21}2017  {c |}{col 28}{res}{space 2}-.1670956{col 40}{space 2} .0350952{col 51}{space 1}   -4.76{col 60}{space 3}0.000{col 68}{space 4}-.2358809{col 81}{space 3}-.0983104
{txt}{space 21}2018  {c |}{col 28}{res}{space 2}-.1854944{col 40}{space 2} .0369068{col 51}{space 1}   -5.03{col 60}{space 3}0.000{col 68}{space 4}-.2578303{col 81}{space 3}-.1131585
{txt}{space 21}2019  {c |}{col 28}{res}{space 2}-.2224807{col 40}{space 2} .0385045{col 51}{space 1}   -5.78{col 60}{space 3}0.000{col 68}{space 4}-.2979481{col 81}{space 3}-.1470133
{txt}{space 21}2020  {c |}{col 28}{res}{space 2}-.0680181{col 40}{space 2} .0477065{col 51}{space 1}   -1.43{col 60}{space 3}0.154{col 68}{space 4}-.1615212{col 81}{space 3}  .025485
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .0772991{col 40}{space 2} .0455813{col 51}{space 1}    1.70{col 60}{space 3}0.090{col 68}{space 4}-.0120386{col 81}{space 3} .1666367
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} -.060891{col 40}{space 2} .0495336{col 51}{space 1}   -1.23{col 60}{space 3}0.219{col 68}{space 4} -.157975{col 81}{space 3} .0361931
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .4467378{col 40}{space 2} .2136492{col 51}{space 1}    2.09{col 60}{space 3}0.037{col 68}{space 4}  .027993{col 81}{space 3} .8654826
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .3051416{col 40}{space 2} .1711046{col 51}{space 1}    1.78{col 60}{space 3}0.075{col 68}{space 4}-.0302172{col 81}{space 3} .6405003
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .1947464{col 40}{space 2} .1755155{col 51}{space 1}    1.11{col 60}{space 3}0.267{col 68}{space 4}-.1492578{col 81}{space 3} .5387505
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .1003627{col 40}{space 2} .1610983{col 51}{space 1}    0.62{col 60}{space 3}0.533{col 68}{space 4}-.2153842{col 81}{space 3} .4161096
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2}-.1506053{col 40}{space 2} .1647675{col 51}{space 1}   -0.91{col 60}{space 3}0.361{col 68}{space 4}-.4735437{col 81}{space 3} .1723331
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2}-.0023714{col 40}{space 2} .1655833{col 51}{space 1}   -0.01{col 60}{space 3}0.989{col 68}{space 4}-.3269087{col 81}{space 3} .3221659
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2}-.0273745{col 40}{space 2} .1597852{col 51}{space 1}   -0.17{col 60}{space 3}0.864{col 68}{space 4}-.3405477{col 81}{space 3} .2857987
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2} .0095017{col 40}{space 2} .1604786{col 51}{space 1}    0.06{col 60}{space 3}0.953{col 68}{space 4}-.3050306{col 81}{space 3} .3240341
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0343983{col 40}{space 2} .1572351{col 51}{space 1}   -0.22{col 60}{space 3}0.827{col 68}{space 4}-.3425736{col 81}{space 3} .2737769
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2} .1807672{col 40}{space 2} .1609385{col 51}{space 1}    1.12{col 60}{space 3}0.261{col 68}{space 4}-.1346663{col 81}{space 3} .4962008
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0068987{col 40}{space 2}  .149185{col 51}{space 1}   -0.05{col 60}{space 3}0.963{col 68}{space 4}-.2992959{col 81}{space 3} .2854985
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0531668{col 40}{space 2} .1492329{col 51}{space 1}   -0.36{col 60}{space 3}0.722{col 68}{space 4}-.3456579{col 81}{space 3} .2393243
{txt}{space 2}adoptcohort_2020_itadopt {c |}{col 28}{res}{space 2} .0348293{col 40}{space 2} .1552339{col 51}{space 1}    0.22{col 60}{space 3}0.822{col 68}{space 4}-.2694235{col 81}{space 3} .3390822
{txt}{space 2}adoptcohort_2021_itadopt {c |}{col 28}{res}{space 2}-.1832366{col 40}{space 2} .1575991{col 51}{space 1}   -1.16{col 60}{space 3}0.245{col 68}{space 4} -.492125{col 81}{space 3} .1256519
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m2e if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m2e if e(sample), residuals
{res}{txt}(6,044 missing values generated)

{com}. 
. gen sse_m2e = predsy_m2e * predsy_m2e if e(sample)
{txt}(6,044 missing values generated)

{com}. gen ssr_m2e = residsy_m2e * residsy_m2e if e(sample)
{txt}(6,044 missing values generated)

{com}. 
. egen sum_sse_m2e = total(sse_m2e) if e(sample)
{txt}(6,043 missing values generated)

{com}. egen sum_ssr_m2e = total(ssr_m2e) if e(sample)
{txt}(6,043 missing values generated)

{com}. 
. gen r2_m2e = sum_ssr_m2e/(sum_sse_m2e + sum_ssr_m2e)
{txt}(6,043 missing values generated)

{com}. 
. sum r2_m2e

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m2e {c |}{res}      6,508    .3020898           0   .3020898   .3020898
{txt}
{com}. 
. 
. 
. * [MODEL E2: RELATIVE TYPE I ERROR RATE : ERRONEOUS DENIAL TYPE II ERRORS] FIGURE E2A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,507}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4479557{col 26}{space 2} .0091259{col 37}{space 1}   49.09{col 46}{space 3}0.000{col 54}{space 4} .4300692{col 67}{space 3} .4658422
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4459775{col 26}{space 2} .0084427{col 37}{space 1}   52.82{col 46}{space 3}0.000{col 54}{space 4}   .42943{col 67}{space 3} .4625249
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4367671{col 26}{space 2}  .006033{col 37}{space 1}   72.40{col 46}{space 3}0.000{col 54}{space 4} .4249427{col 67}{space 3} .4485915
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4271318{col 26}{space 2}  .006061{col 37}{space 1}   70.47{col 46}{space 3}0.000{col 54}{space 4} .4152524{col 67}{space 3} .4390111
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4189178{col 26}{space 2} .0081486{col 37}{space 1}   51.41{col 46}{space 3}0.000{col 54}{space 4} .4029469{col 67}{space 3} .4348888
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4119935{col 26}{space 2} .0105785{col 37}{space 1}   38.95{col 46}{space 3}0.000{col 54}{space 4}   .39126{col 67}{space 3} .4327269
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .4062268{col 26}{space 2} .0127873{col 37}{space 1}   31.77{col 46}{space 3}0.000{col 54}{space 4} .3811641{col 67}{space 3} .4312894
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4014858{col 26}{space 2}   .01465{col 37}{space 1}   27.41{col 46}{space 3}0.000{col 54}{space 4} .3727723{col 67}{space 3} .4301994
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3976389{col 26}{space 2} .0161611{col 37}{space 1}   24.60{col 46}{space 3}0.000{col 54}{space 4} .3659636{col 67}{space 3} .4293141
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .394554{col 26}{space 2} .0173546{col 37}{space 1}   22.73{col 46}{space 3}0.000{col 54}{space 4} .3605395{col 67}{space 3} .4285684
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .3920993{col 26}{space 2} .0182806{col 37}{space 1}   21.45{col 46}{space 3}0.000{col 54}{space 4} .3562699{col 67}{space 3} .4279287
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .390143{col 26}{space 2} .0189968{col 37}{space 1}   20.54{col 46}{space 3}0.000{col 54}{space 4} .3529099{col 67}{space 3}  .427376
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE E2A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Program Error Rate: Erroneous Denial Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E2]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. 
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2A.04-10-2025.gph} saved

{com}. 
. 
. 
. 
. * [MODEL E2: RELATIVE TYPE I ERROR RATE : ERRONEOUS DENIAL TYPE II ERRORS] Figure E2B (relt1_interstate_catE2==2) & LOW COMPLEXITY (relt1_interstate_catE2==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_catE2 if relt1_interstate_catE2==0|relt1_interstate_catE2==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,316}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_catE2@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.82{col 52}{space 2}   0.0030
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     8.86{col 52}{space 2}   0.0029
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     7.37{col 52}{space 2}   0.0066
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     4.09{col 52}{space 2}   0.0432
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     1.95{col 52}{space 2}   0.1621
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.92{col 52}{space 2}   0.3365
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.44{col 52}{space 2}   0.5090
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.20{col 52}{space 2}   0.6561
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.08{col 52}{space 2}   0.7768
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.02{col 52}{space 2}   0.8759
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.00{col 52}{space 2}   0.9589
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.00{col 52}{space 2}   0.9705
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_catE2@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2}-.0501706{col 40}{space 2} .0168903{col 51}{space 5}-.0832749{col 65}{space 3}-.0170663
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2}-.0486328{col 40}{space 2} .0163409{col 51}{space 5}-.0806604{col 65}{space 3}-.0166051
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2}-.0413853{col 40}{space 2} .0152424{col 51}{space 5}-.0712599{col 65}{space 3}-.0115108
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2} -.033614{col 40}{space 2} .0166261{col 51}{space 5}-.0662006{col 65}{space 3}-.0010275
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2}-.0267824{col 40}{space 2} .0191566{col 51}{space 5}-.0643287{col 65}{space 3} .0107639
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2} -.020816{col 40}{space 2} .0216593{col 51}{space 5}-.0632674{col 65}{space 3} .0216354
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2}-.0156406{col 40}{space 2} .0236838{col 51}{space 5}  -.06206{col 65}{space 3} .0307787
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2}-.0111819{col 40}{space 2} .0251142{col 51}{space 5}-.0604048{col 65}{space 3}  .038041
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2}-.0073655{col 40}{space 2} .0259775{col 51}{space 5}-.0582805{col 65}{space 3} .0435495
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2}-.0041172{col 40}{space 2}   .02637{col 51}{space 5}-.0558014{col 65}{space 3} .0475671
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2}-.0013625{col 40}{space 2} .0264277{col 51}{space 5}-.0531599{col 65}{space 3} .0504349
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2} .0009728{col 40}{space 2} .0263123{col 51}{space 5}-.0505984{col 65}{space 3} .0525439
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE E2B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity: Interstate Claims{c )-}" "{c -(}bf: (Relative Type I Program Error Rate: Erroneous Denial Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E2]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2B.04-10-2025.gph} saved

{com}. *
. *
. *
. 
. * [MODEL E2: RELATIVE TYPE I ERROR RATE : ERRONEOUS DENIAL TYPE II ERRORS] Figure E2C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catE2==2) & LOW COMPLEXITY (relt1_diffoccupseek_catE2==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. margins r.relt1_diffoccupseek_catE2 if relt1_diffoccupseek_catE2==0|relt1_diffoccupseek_catE2==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,254}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 30}{c TT}{hline 11}{hline 12}{hline 11}
{col 31}{text}{c |}         df{col 43}        chi2{col 55}     P>chi2
{res}{col 1}{text}{hline 30}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_catE2@_at {c |}
{space 17}(2 vs 0)  1  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}    18.28{col 55}{space 2}   0.0000
{txt}{space 17}(2 vs 0)  2  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}    18.25{col 55}{space 2}   0.0000
{txt}{space 17}(2 vs 0)  3  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}    14.24{col 55}{space 2}   0.0002
{txt}{space 17}(2 vs 0)  4  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     7.35{col 55}{space 2}   0.0067
{txt}{space 17}(2 vs 0)  5  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     3.67{col 55}{space 2}   0.0553
{txt}{space 17}(2 vs 0)  6  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     2.11{col 55}{space 2}   0.1467
{txt}{space 17}(2 vs 0)  7  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.44{col 55}{space 2}   0.2303
{txt}{space 17}(2 vs 0)  8  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.17{col 55}{space 2}   0.2787
{txt}{space 17}(2 vs 0)  9  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.12{col 55}{space 2}   0.2899
{txt}{space 17}(2 vs 0) 10  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.21{col 55}{space 2}   0.2710
{txt}{space 17}(2 vs 0) 11  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.43{col 55}{space 2}   0.2326
{txt}{space 17}(2 vs 0) 12  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.75{col 55}{space 2}   0.1857
{col 1}{text}                       Joint {col 31}{c |}{result}  (not testable)
{col 1}{text}{hline 30}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 31}{c |}{col 43} Delta-method
{col 31}{c |}   Contrast{col 43}   std. err.{col 55}     [95% con{col 68}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_catE2@_at {c |}
{space 17}(2 vs 0)  1  {c |}{col 31}{res}{space 2} .0687528{col 43}{space 2} .0160826{col 54}{space 5} .0372315{col 68}{space 3}  .100274
{txt}{space 17}(2 vs 0)  2  {c |}{col 31}{res}{space 2} .0664115{col 43}{space 2} .0155477{col 54}{space 5} .0359386{col 68}{space 3} .0968845
{txt}{space 17}(2 vs 0)  3  {c |}{col 31}{res}{space 2}  .055947{col 43}{space 2}  .014826{col 54}{space 5} .0268886{col 68}{space 3} .0850055
{txt}{space 17}(2 vs 0)  4  {c |}{col 31}{res}{space 2} .0459668{col 43}{space 2} .0169584{col 54}{space 5}  .012729{col 68}{space 3} .0792046
{txt}{space 17}(2 vs 0)  5  {c |}{col 31}{res}{space 2} .0385596{col 43}{space 2} .0201187{col 54}{space 5}-.0008723{col 68}{space 3} .0779916
{txt}{space 17}(2 vs 0)  6  {c |}{col 31}{res}{space 2}  .033473{col 43}{space 2} .0230642{col 54}{space 5}-.0117321{col 68}{space 3}  .078678
{txt}{space 17}(2 vs 0)  7  {c |}{col 31}{res}{space 2} .0304544{col 43}{space 2} .0253862{col 54}{space 5}-.0193017{col 68}{space 3} .0802105
{txt}{space 17}(2 vs 0)  8  {c |}{col 31}{res}{space 2} .0292514{col 43}{space 2} .0270062{col 54}{space 5}-.0236797{col 68}{space 3} .0821824
{txt}{space 17}(2 vs 0)  9  {c |}{col 31}{res}{space 2} .0296114{col 43}{space 2} .0279779{col 54}{space 5}-.0252242{col 68}{space 3}  .084447
{txt}{space 17}(2 vs 0) 10  {c |}{col 31}{res}{space 2}  .031282{col 43}{space 2} .0284197{col 54}{space 5}-.0244196{col 68}{space 3} .0869836
{txt}{space 17}(2 vs 0) 11  {c |}{col 31}{res}{space 2} .0340107{col 43}{space 2} .0284892{col 54}{space 5}-.0218271{col 68}{space 3} .0898485
{txt}{space 17}(2 vs 0) 12  {c |}{col 31}{res}{space 2}  .037545{col 43}{space 2} .0283709{col 54}{space 5}-.0180609{col 68}{space 3}  .093151
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE E2C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity: Seeking Different Occupation{c )-}" "{c -(}bf:(Relative Type I Program Error Rate: Erroneous Denial Type II Program Error Rate){c )-}" "{c -(}bf:[MODEL E2]{c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-10)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model E2.FIGURE E2C.04-10-2025.gph} saved

{com}. 
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX E MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Apr 2025, 03:28:13
{txt}{.-}
{smcl}
{txt}{sf}{ul off}